Automatic Fatigue Monitoring Based on Real Loads and Consideration of EAF: Live Demonstration

Bergholz, Steffen; Jouan, Benoît; Rudolph, Jürgen; Heinz, Benedikt

doi:10.1115/pvp2013-97236

Cited by 2 publications

(3 citation statements)

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“…The 500 min of simulated time yields a rate of change of chirp frequency of '0:17 mHzs À1 which is quasistationary compared with the sampling rate '0:027 s. Following the computation of the temperature profile, shear wave time of flights (TOFs) were calculated using the trapezoidal approximation 18 given in Equation (2). Source: Adapted from Bergholz et al 16 and Rudolph et al 17 In Equation (2) Dx, v i and N denote the nodal spacing, wave velocity at the i th node, and the total number of nodes respectively. A quadratic fit was used to describe the velocity-temperature v T ð Þ ð Þ relationship.…”

Section: Limitations Of Current Hctf Monitoring Methodsmentioning

confidence: 99%

“…The integrated FAtigue MOnitoring System (FAMOSi) was developed by Siemens in the 1980s and later updated by Areva (a French multinational group with a focus on nuclear power) for thermal fatigue monitoring following the discovery of fatigue cracks in NPPs. 16 The resulting non-invasive system, called Integrated FAMOS (FAMOSi), comprises seven temperature sensors mounted around one half of a pipe’s circumference as shown in Figure 2. The system is based on a comparison of the outer surface temperature-time history to a pre-compiled reference database of ‘responses’ which are computed via a finite element model (FEM).…”

Section: Introductionmentioning

confidence: 99%

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A comparison of ultrasonic temperature monitoring using machine learning and physics-based methods for high-cycle thermal fatigue monitoring

Clarkson,

Zhang,

Cegla

2023

Structural Health Monitoring

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Failure of pipe network components in so-called mixing zones due to high-cycle thermal fatigue (HCTF) can occur within nuclear power plants where fluids of different thermal and hydraulic properties interact. Given that the consequences of such failures are potentially deadly, a method to monitor HCTF non-invasively in real-time is expected to be of great use. This method may be realised by a technique to determine the inaccessible temperature distribution of a component since thermal gradients drive HCTF. Previous work showed that a physics-based method called the inverse thermal modelling (ITM) method can obtain the temperature distribution from external temperature and ultrasonic time of flight (TOF) measurements. This study investigated whether the long-short-term memory (LSTM) machine learning architecture could be a faster alternative to the ITM method for data inversion. On experimental data, a 25-member ensemble of LSTM networks achieved an ensemble median root mean square error (RMSE) of 1.04°C and an ensemble median mean error of 0.194°C (both relative to a resistance temperature device measurement). These values are similar to the ITM method which achieved a RMSE of 1.04°C and a mean error of 0.196°C. The single LSTM network and the ITM method achieved a computation-to-real-world time ratio of 0.008% and 14%, respectively demonstrating that both methods can invert data in real-time. Simulation studies revealed that LSTM performance is sensitive to small differences between the training and real-world parameters leading to unacceptable errors. However, these errors can be detected via an ensemble of independent networks and, corrected by simply adding a correction factor to the TOF prior to being input into the networks. The results show that LSTM has the potential to be an alternative to the ITM method; however, the authors favour ITM for temperature distribution monitoring given its interpretability.

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Section: Limitations Of Current Hctf Monitoring Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comparison of ultrasonic temperature monitoring using machine learning and physics-based methods for high-cycle thermal fatigue monitoring

Clarkson,

Zhang,

Cegla

2023

Structural Health Monitoring

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“…Most previous research on NPP component safety assessment, including our own [11][12], is based on a stress analysis of components using simplified design transients. For accurate structural integrity assessment of NPP components, it is necessary to perform structural fatigue evaluation under more realistic loads [13][14][15]. In this regard, thermal-mechanical stress analysis of NPP components under the grid load-following mode might be necessary for accurate fatigue evaluation.…”

Section: Introductionmentioning

confidence: 99%

Thermal–mechanical stress analysis of pressurized water reactor pressure vessel with/without a preexisting crack under grid load following conditions

Mohanty

Soppet

Majumdar

et al. 2016

Nuclear Engineering and Design

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In this paper, we present thermal-mechanical stress analysis of a pressurized water reactor pressure vessel and its hot-leg and cold-leg nozzles. Results are presented from thermal and thermal-mechanical stress analysis under reactor heat-up, cool-down, and grid load-following conditions. Analysis results are given with and without the presence of preexisting crack in the reactor nozzle (axial crack in hot leg nozzle). From the model results it is found that the stressstrain states are significantly higher in case of presence of crack than without crack. The stressstrain state under grid load following condition are more realistic compared to the stress-strain state estimated assuming simplified transients.

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Automatic Fatigue Monitoring Based on Real Loads and Consideration of EAF: Live Demonstration

Cited by 2 publications

References 0 publications

A comparison of ultrasonic temperature monitoring using machine learning and physics-based methods for high-cycle thermal fatigue monitoring

A comparison of ultrasonic temperature monitoring using machine learning and physics-based methods for high-cycle thermal fatigue monitoring

Thermal–mechanical stress analysis of pressurized water reactor pressure vessel with/without a preexisting crack under grid load following conditions

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